{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diasaggregate your Home/Building Mains Meter Data\n",
    "\n",
    "### This notebook demonstrates the use of siteonlyapi - a new NILMTK interface which is a modification of NILMTK's ExperimentAPI. It allows NILMTK users to get their home/buildings energy demands for different potential appliances.\n",
    "\n",
    "Lets us start with a very simple experiment to demonstrate the use of this API. This experiment shows how the user can convert their meter data into proper REDD format, and call the API  to disaggregate the energy into appliance demands based on the training set.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert meter data into proper format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilmtk.dataset_converters.caxe import convert_caxe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading  1\n",
      "Done converting YAML metadata to HDF5!\n",
      "Done converting test data to HDF5!\n"
     ]
    }
   ],
   "source": [
    "convert_caxe('ac_seconds4.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import different algorithms for disaggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilmtk.disaggregate import Hart85\n",
    "from nilmtk.disaggregate import Mean\n",
    "from nilmtk.disaggregate import CO\n",
    "from nilmtk.disaggregate import FHMMExact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you may change the start and end dates of your test data set and also, we enter the values for the different parameters in the dictionary. Since we need multiple appliances, we enter the names of all the required appliances in the 'appliances' parameter. Also we mention site_only to be true because we want to disaggregate the site meter data only without any comparison with submeter data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment1 = {\n",
    "    'power': {'mains': ['active'],'appliance': ['active']},\n",
    "    'sample_rate': 60,\n",
    "    'appliances': ['air conditioner','fridge','washing machine','clothes iron','television'],\n",
    "    'methods': {\"CO\":CO({}),\"FHMM\":FHMMExact({'num_of_states':2}),'Mean':Mean({}),'Hart':Hart85({})},\n",
    "    'site_only' : True,\n",
    "  'train': {    \n",
    "    'datasets': {\n",
    "        'iAWE': {\n",
    "            'path': './iAWE.h5',\n",
    "            'buildings': {\n",
    "                1: {\n",
    "                    'start_time': '2013-07-13', \n",
    "                    'end_time': '2013-08-04'\n",
    "                    }\n",
    "                }                \n",
    "            }\n",
    "        }\n",
    "    },\n",
    "  'test': {\n",
    "    'datasets': {\n",
    "        'CAXE': {\n",
    "            'path': './test.h5',\n",
    "            'buildings': {\n",
    "                1: {\n",
    "                    'start_time': '2020-08-12',\n",
    "                    'end_time': '2020-08-14'\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        },\n",
    "        'metrics':['rmse']\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilmtk.api import API\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joint Testing for all algorithms\n",
      "Loading data for  CAXE  dataset\n",
      "Dropping missing values\n",
      "Generating predictions for : CO\n",
      "...............CO disaggregate_chunk running.............\n",
      "Generating predictions for : FHMMvision'n'ne'\n",
      "Generating predictions for : Mean\n",
      "Generating predictions for : Hart85\n",
      "Finding Edges, please wait ...\n",
      "Edge detection complete.\n",
      "Creating transition frame ...\n",
      "Transition frame created.\n",
      "Creating states frame ...\n",
      "States frame created.\n",
      "Finished.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zGTFiBBkZGRw5coTc3Fzy8/MBmDNnDqmpqaSkpDBu3LjQ6xs0aMD9999Peno63bp1Y/fu3QDs3buXa6+9lpycHHJyckLbnDhxIqNGjSIvL4+f//zn1fxum7Ol9Es5GLTk7mXhdL36AV+q6l4AEVkI9AB2i0hLt9feEtgTbpAPvbqeDQUHw13NcTq2asSEqztVuszTTz9N06ZNOXLkCDk5OVx77bU0a9bsuGU2b97MrFmzmD59+nHTjx07xvXXX8/cuXPJycnh4MGD1K1bl7/97W8A/Otf/2LTpk3k5eXx2WefAbBu3TrWrl1LQkICl1xyCXfeeSdxcXFMmDCBNWvW0LhxYy677DIyMzOP29awYcOYNm0aU6ZMITs7+7h5BQUFjBs3jjVr1tCkSRPy8vJ46aWXGDJkCIcPH6Zbt25MmjSJsWPH8uSTT/L73/+eu+++m3vvvZdevXqxfft2+vfvz8aNGwFYs2YNy5cvp27dumf+ppsaIaZOHQCCJZbcvSyc5L4d6CYi9XDKMn2BfOAwMBKY7P5+Odwgo2Xq1Km8+OKLAOzYsYMtW7aclNzbtGlDt27dTnrt5s2badmyJTk5OQA0atQIgOXLl3PnnXcC0L59e9q0aRNK7n379qVx48YAdOzYka+++op9+/aRm5tL6X83119/fWj507F69erjXj9ixAiWLVvGkCFDqFOnDldddRUAnTt35u233wbgnXfeOa5ef/DgQQoLCwEYNGiQJfZaLt5N7mplGU8Lp+b+oYjMBz4CAsBa4AmgATBPRH6B8wVwXbhBnqqHHQlLly7lnXfeYeXKldSrV4/c3Nxyz8WuX79+ua9X1XJP8VOt+AOVkJAQehwbG0vAvcgknFMFK9tefHx8aN1ltxcMBlm5cmW5Sbyi9praI66O83emQTsV0svCOvqnqhNUtb2qpqjqTap6VFX3q2pfVW3n/v62uoI9m77//nuaNGlCvXr12LRpEx988MEZvb59+/YUFBSwevVqAAoLCwkEAvTu3Zvnn38egM8++4zt27dzySWXVLierl27snTpUvbv309xcTH/+Mc/yl2uYcOGod71ia9/77332LdvHyUlJcyZM4c+ffpUGnteXh7Tpk0LPV+3bt0p22tqjzi3ExG0k2U8rcZeoRptAwYMIBAIkJaWxgMPPFBu6aUyderUYe7cudx5552kp6dzxRVXUFRUxJgxYygpKSE1NZXrr7+e2bNnH9djP1HLli2ZOHEi3bt3p1+/fmRlZZW73M0338zo0aNDB1TLvv5Pf/oTl112Genp6WRlZTF48OBKY586dSr5+fmkpaXRsWNHZsyYcUZtNzVbXLxblqnkvzpT+0lN2MHZ2dlaemZHqY0bN9KhQ4coRWTONtvfZ0/Bp/m8OO0gLeou4ad/fTja4ZgwiMgaVc0ub5713I3xmbhE51hKDejXmQiy5G6Mz8QnOMk9WGJj+niZJXdjfCaubj3Aeu5eZ8ndGJ+JT0h0Hlhy9zRL7sb4TFwdt+YetLKMl9nIT8b4TFxCAmgQxZK7l1nP/RSmTp1Khw4dGDFixHHT8/Pzueuuu8p9TenAYsbUVKIlqFpy9zLruZ/C9OnTefPNN4+7v2cgECA7O/ukQbqMqS1Eg2DJ3dOs516J0aNH88UXXzBo0CAaN2583HC3S5cuDQ26tX//fvLy8sjMzOSXv/zlcVf+Pfzww7Rv354rrriCG264gSlTpgCwdetWBgwYQOfOnbn00kvZtGlTVNpo/Ek0aD13j6sdPfc3x8M3/6redZ6fCldOrnSRGTNm8NZbb7FkyRKmTZvGq6++GhrudunSpaHlHnroIXr16sWDDz7I66+/Hhr7PT8/nwULFrB27VoCgQBZWVl07twZgFGjRjFjxgzatWvHhx9+yJgxY3j33Xert43GVMh67l5XO5J7DVHRcLfLli1j4cKFAPzkJz+hSRPnnuDLly9n8ODBoddcffXVABw6dIh//vOfXHfdjwNmHj16NNLhGxMidkDV82pHcj9FD/tsqWy42zMZ3jcYDHLOOefYaIsmakRLsKqst9nerQZlh/F98803+e677wDo1asXr776KkVFRRw6dIjXX38dcG7c0bZt29DwvarKxx9/HJ3gjU8FUbWPv5fZ3q0GEyZMYNmyZWRlZbFo0aLQDb9zcnIYNGgQ6enpXHPNNWRnZ4futPT888/z1FNPkZ6eTqdOnXj55Vp7wypTC4kGwcoynmZD/kbYoUOHaNCgAT/88AO9e/fmiSeeqHBMdj/zyv6uLZ685QXidCe3zP5NtEMxYahsyN/aUXOvxUaNGsWGDRsoKipi5MiRlthNjeD03O0fdy+z5B5h//u//xvtEIwph5VlvM6SuzF+pCVAbLSjMBFk/5cZ40OCovbx9zTbu8b4UglWlvE2S+7G+JIdUPU627tnyc0338z8+fNPml5QUMCwYcOiENHpKTtA2oluu+02NmzYcJYjMtUjaGUZjwvrgKqInAPMBFJwbtp1K7AZmAskA9uAn6rqd2FF6WGtWrUqN+nXBjNnzox2CKaKhCBKfLTDMBEU7lf334C3VLU9kA5sBMYDi1W1HbDYfV7rPPLII0ydOhWAe++9l8svvxyAxYsXc+ONNwLwq1/9iuzsbDp16sSECRNCrx0/fjwdO3YkLS2N3/zmx4tEli1bRo8ePbjoootCCX3btm2kpKQAMHv2bK655hoGDBhAu3btGDt2bOi1Tz31FBdffDG5ubncfvvt3HHHHSfFPHHiREaOHEleXh7JycksXLiQsWPHkpqayoABAyguLgbgD3/4Azk5OaSkpDBq1KjQGDiff/45/fr1Iz09naysLLZu3Qo4F2INGzaM9u3bM2LEiNDyubm5lF581qBBA+6//37S09Pp1q0bu3fvBmDv3r1ce+215OTkkJOTw4oVK8LaL6a6WFnG66rccxeRRkBv4GYAVT0GHBORwUCuu9gzwFJgXDhB/nnVn9n0bfWOd96+aXvGdak4rN69e/Poo49y1113kZ+fz9GjRykuLmb58uVceumlAEyaNImmTZtSUlJC3759+eSTT0hKSuLFF19k06ZNiAgHDhwIrXPXrl0sX76cTZs2MWjQoHLLMevWrWPt2rUkJCRwySWXcOeddxIbG8vDDz/MRx99RMOGDbn88stJT08vN+6tW7eyZMkSNmzYQPfu3VmwYAGPPPIIQ4cO5fXXX2fIkCHccccdPPjggwDcdNNNvPbaa1x99dWMGDGC8ePHM3ToUIqKiggGg+zYsYO1a9eyfv16WrVqRc+ePVmxYgW9evU6bruHDx+mW7duTJo0ibFjx/Lkk0/y+9//nrvvvpt7772XXr16sX37dvr378/GjRvPeH+Z6mbJ3evC2bsXAXuBWSKyVkRmikh94DxV3QXg/j63vBeLyCgRyReR/L1794YRRmR07tyZNWvWUFhYSEJCAt27dyc/P5/3338/lNznzZtHVlYWmZmZrF+/ng0bNtCoUSMSExO57bbbWLhwIfXq1Qutc8iQIcTExNCxY8dQz/ZEffv2pXHjxiQmJtKxY0e++uorVq1aRZ8+fWjatCnx8fHHDRV8oiuvvJL4+HhSU1MpKSlhwIABAKSmprJt2zYAlixZQteuXUlNTeXdd99l/fr1FBYW8vXXXzN06FAAEhMTQ7F36dKFpKQkYmJiyMjICK2nrDp16oRq8507dw4t884773DHHXeQkZHBoEGDOHjwIIWFhae/I0xECEFULLl7WTg19zggC7hTVT8Ukb9xBiUYVX0CeAKcsWUqW7ayHnakxMfHk5yczKxZs+jRowdpaWksWbKErVu30qFDB7788kumTJnC6tWradKkCTfffDNFRUXExcWxatUqFi9ezAsvvMC0adNCN+FISEgIrb+iMX3KLhMbG0sgEKhw2cpeHxMTQ3x8fGgo4piYGAKBAEVFRYwZM4b8/HwuuOACJk6cSFFRUaXbKC+m8t6v0m2VXSYYDLJy5cpyx8E30WQ9d68LZ+/uBHaq6ofu8/k4yX63iLQEcH/vCS/E6OnduzdTpkyhd+/eXHrppcyYMYOMjAxEhIMHD1K/fn0aN27M7t27efPNNwGnPv39998zcOBAHnvssWoZs71Lly689957fPfddwQCARYsWFDldRUVFQHQvHlzDh06FKr9N2rUiKSkJF566SXAuXnIDz/8EHbseXl5TJs2LfTcxrCvIcTOlvG6Ku9dVf0G2CEil7iT+gIbgFeAke60kUCtHcv20ksvZdeuXXTv3p3zzjuPxMTEUEkmPT2dzMxMOnXqxK233krPnj0BKCws5KqrriItLY0+ffrw17/+New4Wrduze9+9zu6du1Kv3796NixY2jo4DN1zjnncPvtt5OamsqQIUPIyckJzfv73//O1KlTSUtLo0ePHnzzzTdhxz516lTy8/NJS0ujY8eOzJgxI+x1muqgIDb8gJeFNeSviGTgnApZB/gCuAXnC2MecCGwHbhOVb+tbD1eHvK3upQOHRwIBBg6dCi33nprqD7uBba/z67ZtzxCMcncPuun0Q7FhCFiQ/6q6jqgvBX3DWe95mQTJ07knXfeoaioiLy8PIYMGRLtkEytFkSt5+5pNipkLTFlypRoh2A8RMQGDvM6S+7G+JHY2TJeZ3vXGB8S1MoyHmfJ3Rg/EsWG/PU2S+7G+JDEWM/d6yy5n6GJEyee8uDm7NmzKSgoCD1PTk5m37591RrHwIEDjxu3xpgzIaI2/IDH2d6NgBOTeyS88cYbnHPOOcdNU1WCwWBEt2s8wpK759nercSzzz5LWloa6enp3HTTTSfNX7duHd26dSMtLY2hQ4fy3XffMX/+fPLz8xkxYgQZGRkcOXIEgMcff5ysrCxSU1PZtMkZ4fLw4cPceuut5OTkkJmZycsvOxfzrl+/ni5dupCRkUFaWhpbtmw5adul/w1s27aNDh06MGbMGLKystixYwdz5swhNTWVlJQUxo37cVyeioblNf4jMQoSQ+Do0WiHYiKkVpwK+c0f/8jRjdU75G9Ch/ac/7vfVTh//fr1TJo0iRUrVtC8eXO+/fbki2x//vOf8/jjj9OnTx8efPBBHnroIR577DGmTZvGlClTyM7+8fqu5s2b89FHHzF9+nSmTJnCzJkzmTRpEpdffjlPP/00Bw4coEuXLvTr148ZM2Zw9913M2LECI4dO0ZJSUmlbdm8eTOzZs1i+vTpFBQUMG7cONasWUOTJk3Iy8vjpZdeYsiQIRUOy2v8R8S5Mj1w7AhxZQaGM95hPfcKvPvuuwwbNozmzZsD0LRp0+Pmf//99xw4cIA+ffoAMHLkSJYtW1bh+q655hrg+OFwFy1axOTJk8nIyCA3N5eioiK2b99O9+7d+eMf/8if//xnvvrqq1OOqNimTRu6desGwOrVq8nNzaVFixbExcUxYsSIUFwVDctr/McdwJNjR8IfHM7UTLWi515ZDztSVDU0hG11KB02t+xwuKrKggULuOSSS45btkOHDnTt2pXXX3+d/v37M3PmzNCdoMpTv3794+KuSEXD8hr/KS23B4osuXuV9dwr0LdvX+bNm8f+/fsBTirLNG7cmCZNmvD+++8DzoiKpb34hg0bntYNKfr378/jjz8eSshr164F4IsvvuCiiy7irrvuYtCgQXzyySenHXfXrl1577332LdvHyUlJcyZMycUlzGlJMb5mysuOhLlSEyk1IqeezR06tSJ+++/nz59+hAbG0tmZiazZ88+bplnnnmG0aNH88MPP3DRRRcxa9YsAG6++WZGjx5N3bp1WblyZYXbeOCBB7jnnntIS0tDVUlOTua1115j7ty5PPfcc8THx3P++eeHbol3Olq2bMmf/vQnLrvsMlSVgQMHMnjw4Cq9B8a7SnvuxUeLohuIiZiwhvytLjbkr7H9fXb94zcPsOfQZVx9ewIXdu4Z7XBMFVU25K+VZYzxoZgY59iL9dy9y5K7MT5UOvJA4Jid5+5VltyN8aGYWOejHzh6LMqRmEix5G6MD0lpWcZ67p5lyd0YH5JYJ7mrXevgWZbcjfGh0gOqgeLiKEdiIsWSeyUOHDjA9OnTT7lcgwYNqrT+goIChg0bVukyPXr0qNK6jalMbJzz0S85Zl5LwWUAAAxfSURBVMndqyy5V+J0k3tVtWrVivnz51e6zD//+c+Ibd/4V0ycc7pMSbGVZbzKknslxo8fz9atW8nIyOC3v/0tf/nLX8jJySEtLY0JEyaU+5rylhk3btxxXxITJ07k0UcfZdu2baSkpAAVD/Nb+l+BqvLb3/6WlJQUUlNTmTt3LgBLly4lNzeXYcOG0b59e0aMGFHp+DLGAMSU9twtuXtWrRh+4P15n7Fvx6FqXWfzCxpw6U8vrnSZyZMn8+mnn7Ju3ToWLVrE/PnzWbVqFarKoEGDWLZsGb179w4tv2jRIrZs2XLSMsOHD+eee+5hzJgxAMybN4+33nrruBtrnGqY34ULF7Ju3To+/vhj9u3bR05OTmjba9euZf369bRq1YqePXuyYsUKevXqVV1vlfEgcU+FDJbYzV28Kuyeu4jEishaEXnNfd5URN4WkS3u7ybhhxl9ixYtYtGiRWRmZpKVlcWmTZtOuolGRctkZmayZ88eCgoK+Pjjj2nSpAkXXnjhca891TC/y5cv54YbbiA2NpbzzjuPPn36sHr1agC6dOlCUlISMTExZGRk2FC+5pRi45x+XYmdLeNZ1dFzvxvYCDRyn48HFqvqZBEZ7z4fV9GLT8epethng6py33338ctf/rJKywwbNoz58+fzzTffMHz48JPm/+xnP6t0mN/KSi0JZW62YEP5mtMR69bcg4HKbwRjaq+weu4ikgT8BJhZZvJg4Bn38TPAkHC2EU1lh+7t378/Tz/9NIcOOeWhr7/+mj179hy3fGXLDB8+nBdeeIH58+eXe4bMqYb57d27N3PnzqWkpIS9e/eybNkyunTpUu1tNv4QG+/064IBK8t4Vbg998eAsUDDMtPOU9VdAKq6S0TOLe+FIjIKGAWcVKKoKZo1a0bPnj1JSUnhyiuv5Gc/+xndu3cHnAOdzz33HOee+2Pz8vLy2LhxY7nLdOrUicLCQlq3bk3Lli1P2taphvkdOnQoK1euJD09HRHhkUce4fzzzw/dj9WYM/Fjcreeu1dVechfEbkKGKiqY0QkF/iNql4lIgdU9Zwyy32nqpXW3W3IX2P7++xa9cIsVi9tQ/IFK/jJ/Q9EOxxTRZUN+RtOz70nMEhEBgKJQCMReQ7YLSIt3V57S2BPpWsxxpx1cfHxAARL7LRZr6pyzV1V71PVJFVNBoYD76rqjcArwEh3sZHAy2FHaYypVjF13OQesOTuVZG4iGkycIWIbAGucJ9XiV2M4w+2n8+++DrOGVYatPfeq6rlIiZVXQosdR/vB/qGu87ExET2799Ps2bNEJFwV2dqKFVl//79JCYmRjsUX4lLqAPYRUxeVmOvUE1KSmLnzp3s3bs32qGYCEtMTCQpKSnaYfhKbLyT3NVyu2fV2OQeHx9P27Ztox2GMZ4UVycBKEbtgKpn2cBhxvhQnFsGC1rP3bMsuRvjQ/EJTnK3sox3WXI3xofiE52B6TRoJyt4lSV3Y3woNt7tudtpqJ5lyd0YH6pTtx5gPXcvs+RujA/F160PgKold6+y5G6MD8UlJIAGrefuYTX2PHdjTGSJBgFL7l5lyd0YnxINYodTvcvKMsb4VtBq7h5mPXdjfMopy1j/zqssuRvjU6IlqNXcPcuSuzF+ZT13T7M9a4xPidXcPc2SuzF+ZT13T7M9a4xPOT13SwFeZXvWGL/SEuwiJu+y5G6MTwlBsJ67Z9meNca3rObuZbZnjfErO6DqabZnjfEpIYhaCvAs27PG+Jb13L2syntWRC4QkSUislFE1ovI3e70piLytohscX83qb5wjTHVp8R67h4Wzp4NAP+hqh2AbsCvRaQjMB5YrKrtgMXuc2NMDSMo1nP3rirvWVXdpaofuY8LgY1Aa2Aw8Iy72DPAkHCDNMZEgpVlvKxa9qyIJAOZwIfAeaq6C5wvAODcCl4zSkTyRSR/79691RGGMeaMWHL3srD3rIg0ABYA96jqwdN9nao+oarZqprdokWLcMMwxpwhO1vG28LasyISj5PYn1fVhe7k3SLS0p3fEtgTXojGmIiQEpDYaEdhIiScs2UEeArYqKr/VWbWK8BI9/FI4OWqh2eMiRw7oOpl4dysoydwE/AvEVnnTvsdMBmYJyK/ALYD14UXojEmEoQgKjZwmFdVObmr6nIqHlKub1XXa4w5SyQIWFnGq+x/MmN8S1GxFOBVtmeN8SkROxXSy+wG2cb4lQRRK8t4ln1tG+NTgqJ2JybPsp67MX4lih1Q9S5L7sb4lIjaFaoeZnvWGL+KsbNlvMx67sb4lNNzt7KMV9nXtjE+JShIDCWBQLRDMRFgyd0Yv3I//YGiI9GNw0SEJXdjfEpiFIBjRYejHImJBEvuxvhU6ZhhgSM/RDcQExGW3I3xqZhYp+defNTKMl5kyd0YnyrtuZcUH4tuICYiLLkb41Olp7gXFxVFNxATEZbcjfEpiXG67oGjlty9yJK7MT4V416/FDh6NLqBmIiw5G6MT0mM8/EPHLPk7kWW3I3xKXF77iXFxdENxESEJXdjfCom1vn4lxyzs2W8yJK7MT4VE+scUC22nrsnWXI3xqdi4pzkHjxmA4d5kSV3Y3wqNtYpuges5+5JltyN8Slxa+7BkpIoR2IiIWLJXUQGiMhmEflcRMZHajvGmKqJiXN67sFiK8t4UUSSu4jEAv8NXAl0BG4QkY6R2JYxpmpi49yzZQLWc/eiSN1mrwvwuap+ASAiLwCDgQ3lLfzF3sNc/z8rIxSKMaY8Gdu/pxHw+ZoL+eLm56MdjqlmkUrurYEdZZ7vBLqWXUBERgGjABq0/LcIhWGMqciW8zPp+c2HQJ1oh2IiQFS1+lcqch3QX1Vvc5/fBHRR1TvLWz47O1vz8/OrPQ5jjPEyEVmjqtnlzYvUAdWdwAVlnicBBRHaljHGmBNEKrmvBtqJSFsRqQMMB16J0LaMMcacICI1d1UNiMgdwP8DYoGnVXV9JLZljDHmZJE6oIqqvgG8Ean1G2OMqZhdoWqMMR5kyd0YYzzIkrsxxniQJXdjjPGgiFzEdMZBiBQCm6Mdx1nSHNgX7SDOEmur9/ilnVA72tpGVVuUNyNiZ8ucoc0VXWXlNSKSb231Hr+01S/thNrfVivLGGOMB1lyN8YYD6opyf2JaAdwFllbvckvbfVLO6GWt7VGHFA1xhhTvWpKz90YY0w1suRujDEeFPXk7tUbaYvIBSKyREQ2ish6Ebnbnd5URN4WkS3u7ybRjrW6iEisiKwVkdfc555sq4icIyLzRWSTu3+7e7it97p/v5+KyBwRSfRKW0XkaRHZIyKflplWYdtE5D43T20Wkf7Rifr0RTW5e/xG2gHgP1S1A9AN+LXbtvHAYlVtByx2n3vF3cDGMs+92ta/AW+pansgHafNnmuriLQG7gKyVTUFZ/ju4XinrbOBASdMK7dt7md3ONDJfc10N3/VWNHuuYdupK2qx4DSG2nXeqq6S1U/ch8X4iSA1jjte8Zd7BlgSHQirF4ikgT8BJhZZrLn2ioijYDewFMAqnpMVQ/gwba64oC6IhIH1MO5o5on2qqqy4BvT5hcUdsGAy+o6lFV/RL4HCd/1VjRTu7l3Ui7dZRiiRgRSQYygQ+B81R1FzhfAMC50YusWj0GjAWCZaZ5sa0XAXuBWW4JaqaI1MeDbVXVr4EpwHZgF/C9qi7Cg20to6K21bpcFe3kLuVM89S5mSLSAFgA3KOqB6MdTySIyFXAHlVdE+1YzoI4IAv4v6qaCRym9pYlKuXWmwcDbYFWQH0RuTG6UUVNrctV0U7unr6RtojE4yT251V1oTt5t4i0dOe3BPZEK75q1BMYJCLbcEprl4vIc3izrTuBnar6oft8Pk6y92Jb+wFfqupeVS0GFgI98GZbS1XUtlqXq6Kd3D17I20REZy67EZV/a8ys14BRrqPRwIvn+3Yqpuq3qeqSaqajLMP31XVG/FmW78BdojIJe6kvsAGPNhWnHJMNxGp5/4998U5duTFtpaqqG2vAMNFJEFE2gLtgFVRiO/0qWpUf4CBwGfAVuD+aMdTje3qhfNv2yfAOvdnINAM5yj8Fvd302jHWs3tzgVecx97sq1ABpDv7tuXgCYebutDwCbgU+DvQIJX2grMwTmWUIzTM/9FZW0D7nfz1GbgymjHf6ofG37AGGM8KNplGWOMMRFgyd0YYzzIkrsxxniQJXdjjPEgS+7GGONBltyNMcaDLLkbY4wH/X9vDUInqZMbVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "api_results_experiment_1 = API(experiment1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = (api_results_experiment_1.pred_overall['CO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting Predictions Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>air conditioner</th>\n",
       "      <th>fridge</th>\n",
       "      <th>washing machine</th>\n",
       "      <th>clothes iron</th>\n",
       "      <th>television</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows Ã— 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     air conditioner  fridge  washing machine  clothes iron  television\n",
       "0                0.0    84.0              0.0           0.0        70.0\n",
       "1                0.0    84.0              0.0           0.0        74.0\n",
       "2                0.0    84.0              0.0           0.0        74.0\n",
       "3                0.0    84.0              0.0           0.0        74.0\n",
       "4                0.0    84.0              0.0           0.0        74.0\n",
       "..               ...     ...              ...           ...         ...\n",
       "110              0.0     0.0              0.0           0.0         0.0\n",
       "111              0.0     0.0              0.0           0.0         0.0\n",
       "112              0.0     0.0              0.0           0.0         0.0\n",
       "113              0.0     0.0              0.0           0.0         0.0\n",
       "114              0.0     0.0            219.0           0.0        74.0\n",
       "\n",
       "[115 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  }
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